Prediction of English Vocabulary Learning Difficulty and Adjustment of Teaching Strategies Based on Decision Tree Algorithm

Curriculum difficulty is a reflection of education standard and affects the competitiveness of national education, how to scientifically assess the difficulty of English vocabulary learning to eliminate unreasonable difficulty factors has become a problem that must be solved in the current curriculum reform. This paper improves the traditional clustering algorithm and proposes to cluster analyze the difficulty of English vocabulary learning based on DBSCAN algorithm. After introducing the optimization algorithm based on the ID3 algorithm and C4.5 algorithm, the C4.5 algorithm is optimized accordingly. The information gain is replaced with the information gain rate for attribute selection calculation, and on the other hand, the balance coefficient is introduced into the attribute split information quantity to realize the accurate prediction of English vocabulary learning difficulty. Algorithmic validation of the English vocabulary learning difficulty prediction method based on DBSCAN clustering and improved C4.5 algorithm is carried out, and the accuracy of the classification prediction model reaches 0.988, and the speed and accuracy of this algorithm are both improved to different degrees. On the concept of OBE education, the English vocabulary teaching strategy is adjusted accordingly, and the learning difficulty prediction method and the adjusted teaching strategy are applied to teaching, and the results show that the method of this paper can effectively improve the students’ attitude towards English vocabulary learning.